#! /usr/bin/python
# -*- coding: utf8 -*-

import tensorflow as tf
import numpy as np

config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.InteractiveSession(config=config)

from tensorflow.examples.tutorials.mnist import  input_data
mnist = input_data.read_data_sets('/tmp/data/mnist',one_hot=True)
print('training data shape ',mnist.train.images.shape)
print('training label shape ',mnist.train.labels.shape)

def weight_variable(shape):
    #正太分布
    initial = tf.truncated_normal(shape,stddev=0.1)
    return tf.Variable(initial)

def bias_variable(shape):
    initial = tf.constant(0.1,shape=shape)
    return tf.Variable(initial)

X_ = tf.placeholder(tf.float32,[None,784])
y_ = tf.placeholder(tf.float32,[None,10])

# FC1
W_fc1 = weight_variable([784, 1024])
b_fc1 = bias_variable([1024])
h_fc1 = tf.nn.relu(tf.matmul(X_, W_fc1) + b_fc1)

# FC2
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
y_pre = tf.nn.softmax(tf.matmul(h_fc1, W_fc2) + b_fc2)

# 1.损失函数：cross_entropy
cross_entropy = -tf.reduce_sum(y_ * tf.log(y_pre))
# 2.优化函数：AdamOptimizer, 优化速度要比 GradientOptimizer 快很多
train_step = tf.train.AdamOptimizer(0.001).minimize(cross_entropy)

# 3.预测结果评估
#　预测值中最大值（１）即分类结果，是否等于原始标签中的（１）的位置。argmax()取最大值所在的下标
correct_prediction = tf.equal(tf.argmax(y_pre, 1), tf.arg_max(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

# 开始运行
sess.run(tf.global_variables_initializer())
# 这大概迭代了不到 10 个 epoch， 训练准确率已经达到了0.98
for i in range(5000):
    X_batch, y_batch = mnist.train.next_batch(batch_size=100)
    train_step.run(feed_dict={X_: X_batch, y_: y_batch})
    if (i+1) % 200 == 0:
        train_accuracy = accuracy.eval(feed_dict={X_: mnist.train.images, y_: mnist.train.labels})
        print ("step %d, training acc %g" % (i+1, train_accuracy))
    if (i+1) % 1000 == 0:
        test_accuracy = accuracy.eval(feed_dict={X_: mnist.test.images, y_: mnist.test.labels})
        print ("= " * 10, "step %d, testing acc %g" % (i+1, test_accuracy))